Neurobiologically-Based Subtyping of Multi-Cohort Samples with MDD and PTSD Symptoms

具有 MDD 和 PTSD 症状的多队列样本的基于神经生物学的亚型

• 批准号: 10609903
• 负责人: RAJENDRA A MOREY
• 金额: $ 55.95万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-15 至 2026-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10609903
• 关键词: Age; Algorithms; Arousal; Attention; Behavior; Biological Testing; Brain; Brain region; Categories; Characteristics; Chronic; Classification; Clinical; Collaborations; Control Groups; Coupled; Data; Data Set; Development; Diagnosis; Diagnostic; Dimensions; Disease; Disease model; Dorsal; Exposure to; Fright; Functional Magnetic Resonance Imaging; Future; Gender; Goals; Heterogeneity; Hybrids; Individual; Infrastructure; Life Stress; Major Depressive Disorder; Maps; Medical; Mental disorders; Modeling; Modernization; Neurobiology; Participant; Patients; Pattern; Population; Post-Traumatic Stress Disorders; Recording of previous events; Research; Research Domain Criteria; Resources; Rest; Sampling; Severity of illness; Sleep disturbances; Stress; Stressful Event; Symptoms; Syndrome; Technology; Testing; Time; Trauma; algorithm training; biobank; biotypes; brain circuitry; brain dysfunction; cohort; comorbidity; depressive symptoms; disease classification; effective therapy; experience; interest; large datasets; learning strategy; neural; neural circuit; neurobiological mechanism; neuropsychiatric symptom; novel; patient subsets; pediatric trauma; precision medicine; psychiatric comorbidity; psychological trauma; reward processing; supervised learning; symptom cluster; therapy design; translational neuroscience; trauma exposure; unsupervised learning

ABSTRACT Significant symptom overlap and high rates of co-occurrence between syndromes of posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) call into question whether the two are distinct disorders. The onset and course of both syndromes are strongly influenced by environmental variables. We hypothesize that a continuum of life stress or adversity and an independent continuum of psychological trauma conspire to influence the onset of PTSD and MDD (where at least one trauma exposure is required for PTSD). Our overarching goal is to identify and compare neural signatures of MDD, PTSD, symptom features common to PTSD and MDD, and heretofore unrecognized neurobiologically-defined syndromes. Therefore, we plan to investigate neural signatures with supervised learning, and to identify biotypes that cut across disorders (PTSD and MDD) with unsupervised learning, an approach that can better explain contributions of trauma, stressful life events, and disease characteristics than possible with DSM-disorders. Rather than subtyping patients on the basis of clinical symptoms or DSM-defined diagnoses, our goal is to identify distinct clusters of neurobiological subtypes with disrupted neural signatures derived from resting-state fMRI. In Aim 1 we propose to train algorithms with supervised learning to detect neural signatures from resting fMRI data that can classify DSM diagnosis of comorbid PTSD and MDD, PTSD only, MDD only, and Controls (no psychiatric disorder). The analysis will be performed separately with MDD and Control groups who experienced criterion-A trauma or stressful life events, and those who did not. In Aim 2, we plan to use supervised learning in MDD and PTSD patients to identify neural signatures from resting-state fMRI data associated with four trans-diagnostic symptoms that include disrupted sleep, irritability, concentration difficulties, and loss of interest. In Aim 3, we propose to apply unsupervised learning methods to identify novel biotypes associated with specific symptoms or symptom clusters. The algorithms will employ rsfMRI features in patients with (1) PTSD only, (2) MDD only and (2) across PTSD, MDD, and comorbid PTSD+MDD patients in order to identify potential trans-diagnostic biotypes that cut across DSM boundaries. We will investigate associations of diagnosis-specific and trans- diagnostic biotypes derived from unsupervised learning with stressful life events, trauma exposure, developmental stage at time of exposure, psychiatric comorbidities, medical comorbidities, illness chronicity, illness severity, gender, and age. The overlapping and intersecting patterns that maps circuit disruption to psychiatric syndromes presents a daunting challenge in designing treatments that intervene at the circuit level. Developing a neurobiologically-based nosology that maps to clinical symptoms and syndromes represents a major advance in translational neuroscience. The advent of modern brain stimulation technology offers an unprecedented possibility of intervening at the circuit level with precision medicine strategies.

摘要 创伤后应激综合征之间的症状重叠和高共现率 创伤后应激障碍（PTSD）和重度抑郁症（MDD）的区别引起了人们对这两种疾病是否不同的质疑。 这两种综合征的发病和病程都受到环境变量的强烈影响。我们假设 连续的生活压力或逆境和独立的连续的心理创伤共同作用， 影响PTSD和MDD的发作（其中PTSD需要至少一次创伤暴露）。我们 总体目标是识别和比较MDD，PTSD，常见症状特征的神经特征， 创伤后应激障碍和抑郁症，以及迄今未被认识的神经生物学定义的综合征。因此，我们计划 研究神经签名与监督学习，并确定生物型，跨越障碍（创伤后应激障碍 和MDD）与无监督学习，这种方法可以更好地解释创伤，压力和抑郁症的贡献。 生活事件和疾病特征，而不是DSM疾病。而不是对患者进行分型， 临床症状或DSM定义的诊断的基础上，我们的目标是确定不同的集群 神经生物学亚型与中断的神经签名来自静息态功能磁共振成像。在目标1中，我们提出 用监督学习训练算法，从静息功能磁共振成像数据中检测神经特征， 共病PTSD和MDD的DSM诊断、仅PTSD、仅MDD和对照（无精神障碍）。 将分别对MDD组和对照组进行分析， 压力大的生活事件和没有压力的人。在目标2中，我们计划在MDD和PTSD中使用监督学习 患者从静息状态fMRI数据中识别与四种跨诊断相关的神经特征， 症状包括睡眠中断、易怒、注意力难以集中和失去兴趣。在目标3中，我们 我建议应用无监督学习方法来识别与特定症状相关的新生物型 或症状群。该算法将在（1）仅患有PTSD，（2）仅患有MDD的患者中采用rsfMRI特征 （2）在PTSD、MDD和PTSD+MDD共病患者中，以确定潜在的跨诊断 跨越DSM边界的生物型。我们将研究诊断特异性和跨- 来自无监督学习的诊断生物型，有压力的生活事件，创伤暴露， 暴露时的发育阶段、精神病合并症、医学合并症、疾病慢性化， 疾病严重程度、性别和年龄。将电路中断映射到 精神病综合征在设计回路水平干预的治疗方面提出了令人生畏的挑战。 开发一种基于神经生物学的疾病分类学，映射到临床症状和综合征， 转化神经科学的重大进展现代脑刺激技术的出现提供了一个 在电路一级进行干预的可能性是前所未有的。